# A tibble: 359 × 2
sample_name sample_tags
<chr> <chr>
1 310183_TCRB 06 Years, 6 Years at visit, Caucasian, Control, Control 022, GAD…
2 310110_TCRB 0 Years at visit, 09 Months, 6.37808219178082 Years at diagnosis…
3 310206_TCRB 0 Years at visit, 10 Months, 10.558904109589 Years at diagnosis,…
4 310288_TCRB 09 Years, 9 Years at visit, 9.56986301369863 Years at diagnosis,…
5 310271_TCRB 02 Years, 2 Years at visit, 6.87397260273973 Years at diagnosis,…
6 310244_TCRB 05 Years, 18.0438356164384 Years at diagnosis, 5 Years at visit,…
7 310137_TCRB 07 Years, 7 Years at visit, Caucasian, Control, Control 014, Fem…
8 310258_TCRB 12 Years, 12 Years at visit, 12.958904109589 Years at diagnosis,…
9 310127_TCRB 04 Years, 11.6821917808219 Years at diagnosis, 4 Years at visit,…
10 310300_TCRB 17 Years, 17 Years at visit, 17.7561643835616 Years at diagnosis…
# ℹ 349 more rowsDevelopment of T cell receptor repertoires during childhood in health and disease
By Emma A., Bunia A., Othilia FS., Tobias C. & Joanna R.
Group 21
Introduction
Based on the article Mitchell et al. (2022)
Allele repertoire development during childhood and type 1 diabetes
Two groups of patients, case and control
Returns four times for observations
Materials:
1. Data from immunoSEQ ANALYZER
Methods:
1. Base R
1. Tidyverse R
2. PCA analysis
3. MCA analysis
Data Description
Original data (01_dat_load.tsv)
Data Wrangling
Augmented data (03_dat_aug.tsv)
# A tibble: 10 × 24
sample_name total_templates productive_templates fraction_productive
<chr> <dbl> <dbl> <dbl>
1 310102_TCRB 121173 96410 0.796
2 310102_TCRB 121173 96410 0.796
3 310102_TCRB 121173 96410 0.796
4 310102_TCRB 121173 96410 0.796
5 310102_TCRB 121173 96410 0.796
6 310102_TCRB 121173 96410 0.796
7 310102_TCRB 121173 96410 0.796
8 310102_TCRB 121173 96410 0.796
9 310102_TCRB 121173 96410 0.796
10 310102_TCRB 121173 96410 0.796
# ℹ 20 more variables: total_rearrangements <dbl>,
# productive_rearrangements <dbl>, productive_simpson_clonality <dbl>,
# max_productive_frequency <dbl>, locus <chr>, sku <chr>, test_name <chr>,
# case_control <chr>, id <dbl>, timepoint <dbl>, gender <chr>,
# ethnicity <chr>, race <chr>, age <dbl>, age_diagnosis <dbl>,
# age_visit <dbl>, antibody <chr>, expression <dbl>, allele <chr>,
# subtype <chr>Antibody Expression
Antibody Expression, PCA
HLA Expression
HLA Expression, PCA
Discussion
Discuss the implications of your findings. Interpret the results and relate them to your objectives.
Implications of findings
- Patterns exist between the different mean antibody expressions. Suggests need for PCA. .
- Two components explain majority of variance in data. Components are used to express the rotation matrix.
- The rotation matrix shows the variance between the variables. Two clusters form between the four variables, each more strong associated with each of the two component.
- The HLA analysis suggests alleles of interest, by indicating dominant subtypes in case subjects, compared to control.
- MCA…
Limitations
- Missing values for control subjects
Conclusion
- Clear differences exist between case and control subjects, both in terms of antibody expression and in terms of allele subtypes.
- Suggests that both antibody expression and allele subtype are involved in the development of diabetes in children.
- Based on our results,
HLA-DQB1*0202,HLA-B*4402,HLA-A*2402, andHLA-B*0702could have a direct influence on type 1 diabetes.
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